Y.P. Shiga, A.M. Michalak, S.R. Kawa and R.J. Engelen

Inverse models rely on atmospheric carbon dioxide (CO2) measurements to quantify and explore sources and sinks of CO2 at the Earth’s surface. However, the spatial coverage of atmospheric CO2 observations is quite poor and determining where to place new measurements involves either complex simulations or subjective decisions. This study develops a new criterion based on the spatial variability of atmospheric carbon dioxide that is both flexible and computationally efficient while maintaining quantitative objectivity. The criterion is used to evaluate the growth in the North American CO2 observation network from 2004-2008 and to create two hypothetical network expansion scenarios.

Figure: Expansion of the existing tower network (black stars) from 2004 to 2008. Colors represent degree of coverage and are based on each grid cell's distance to nearest tower (hi) divided by the grid cell's correlation length (CL).

Abstract

Estimates of surface fluxes of carbon dioxide (CO2) can be derived from atmospheric CO2 concentration measurements through the solution of an inverse problem, but the sparseness of the existing CO2 monitoring network is often cited as a main limiting factor in constraining fluxes. Existing methods for assessing or designing monitoring networks either primarily rely on expert knowledge, or are sensitive to the large number of modeling choices and assumptions inherent to the solution of inverse problems. This study proposes a monitoring network evaluation and design approach based on the quantification of the spatial variability in modeled atmospheric CO2. The approach is used to evaluate the 2004–2008 North American network expansion and to create two hypothetical further expansions. The less stringent expansion guarantees a monitoring tower within one correlation length (CL) of each location (1 CL), requiring an additional eight towers relative to 2008. The more stringent network includes a tower within one half of a CL (½ CL) and requires 35 towers beyond the 1 CL network. The two proposed networks are evaluated against the network in 2008, which temporarily had the most continuous monitoring sites in North America thanks to the Mid-Continent Intensive project. Evaluation using a synthetic data inversion shows a marked improvement in the ability to constrain both continental- and biome-scale fluxes, especially in areas that are currently under-sampled. The proposed approach is flexible, computationally inexpensive, and provides a quantitative design tool that can be used in concert with existing tools to inform atmospheric monitoring needs.